This repository contains scripts and processed datasets used to train multilayer regression models—specifically, Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) architectures. The primary goal is to identify single nucleotide polymorphisms (SNPs) that have the highest impact on a given phenotype.
In this study, we explore the application of advanced deep learning techniques for feature selection in genomic data. By training MLP and CNN regression models, we aim to pinpoint key SNPs that significantly influence phenotypic variation. The models are developed and evaluated using processed datasets, and the results help guide downstream analysis in genetic studies.